认知MIMO网络资源分配关键技术研究
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摘要
随着新的无线通信的快速发展，对频谱资源的需求日益增长，使得频谱成为稀缺的资源。然而，当前的固定频谱分配政策造成对频谱资源的低效利用，已成为未来无线通信发展的瓶颈。近年来，新兴的认知无线电（CR）技术使得未授权用户或称为次用户（SU）能够通过共享授权用户或称为主用户（PU）的频谱的方式来提高频谱利用率，为新出现的无线业务和应用提供带宽，成为解决频谱资源稀缺的一种有效途径。
MIMO技术可带来空间分集与复用增益，能够显著地提高无线通信系统的传输可靠性以及频谱效率，是未来无线通信的关键技术之一。同时，将MIMO技术引入到认知无线电网络中，将在时间、频率之外为认知无线电接入授权频谱的行为带来额外的空间自由度。因此，对MIMO与认知无线电相结合，研究认知无线电环境下的MIMO网络，即认知MIMO网络，显得尤其重要。
在认知无线电环境中，为保护PU的信号传输，SU不能对PU造成有害干扰。基本上，SU利用PU频谱的方式可分为以下两种：一种是机会频谱接入（OSA），SU只接入未被PU占用的授权频谱；另一种是共存频谱接入（CSA），SU与PU同时共存，但需要抑制SU信号对PU的干扰在给定范围内，即满足干扰/干扰温度约束。
本文关注认知MIMO网络，其中SU装备多根天线或通过用户间协作形成虚拟多天线。本文研究不同网络架构下的认知MIMO网络的资源分配与管理问题。具体而言，我们考虑在保障PU的QoS的前提下，如何合理配置通信资源，包含发送功率控制、波束成形权值与预编码矩阵设计，从而使SU能够在对PU干扰最小化条件下，最有效的共享授权频谱，如从中获取最大的信息速率、信噪比（SNR）与吞吐量等。本文主要工作和贡献可概述如下：
（1）提出一种认知MIMO多址接入信道（MAC）中的最优发送协方差矩阵设计算法。我们考虑由多个多天线SU发送端与一个多天线SU共同接收端构成的认知MIMO MAC场景。研究了在给定SU节点发送功率与干扰温度约束下，如何选择SU的发送协方差矩阵使得认知MIMO MAC的速率之和最大化，解决优化问题时，利用部分对偶分解技术松弛干扰温度约束，将原始和速率最大化问题分解为两个相互联系的子问题。文中提出一种迭代算法，通过交替进行对偶变量更新与顺序迭代注水运算求解分解后的子问题，得到使得和速率最大的最优SU发送协方差矩阵。
（2）研究认知MIMO干扰信道（IC）中的波形自适应技术。我们考虑由多个独立的SU节点对构成的认知MIMO IC中的波形自适应问题，其中每个节点对都包含有一个多天线SU发送端与多天线SU接收端。我们使用了非合作博弈理论，在发送功率以及PU干扰温度共同约束下，针对多个SU为最大化自身链路信息速率进行的波形自适应进行建模，将其表示为Nash均衡问题；通过将Nash均衡问题转换为等价的变分不等式问题，推导了Nash均衡唯一存在的充分条件；提出了一种带价格系数的分布式发送功率迭代注水算法，即MIMO-CRIWFA，用以解决上述Nash均衡问题；通过价格机制，即使用惩罚价格使得所有参与通信的SU改变发送波形，控制对PU的叠加干扰，从而在满足PU的干扰温度约束时达到Nash均衡，最大化各自信息速率；同时给出了MIMO-CR IWFA算法收敛到唯一Nash均衡解的条件。
（3）研究认知中继网络中的最优源功率控制与协作波束成形技术。我们考虑多个单天线的SU通过协作组成虚拟多天线，来获得多天线系统所具有的空间分集性能，这样的SU虚拟多天线系统即认知中继网络。我们分别研究了在干扰温度约束下以及干扰温度与发送功率共同约束下的SU源节点功率控制与SU中继节点协作波束成形问题，优化的目标是使得SU目的节点的SNR最大。1）干扰温度约束下的源功率控制与中继协作波束成形问题被转化为仅与源发送功率相关的广义Rayleigh商问题，最优波束成形权值为一个与源发送功率以及信道系数相关的矩阵的最大特征值所对应的特征向量，原始多变量问题被简化为求解源节点最优发送功率的单变量问题。文中给出了严格干扰温度约束下的解析解，对于宽松干扰温度约束，文中进一步给出了包含最优发送功率的紧缩区间，并在该区间内使用了基于最速下降的梯度方法求解。2）对于发送功率与干扰温度共同约束，我们首先解决在给定任意认知源节点发送功率时，如何计算相应的认知中继最优协作波束成形权值。通过Dinkelbach型方法，这个问题被转化为一系列二次约束二次规划（QCQP）问题。利用半定松弛技术，这些QCQP问题被转变为易于解决的半定规划（SDP）问题从而可通过内点方法求解。随后，我们使用了一种基于粒子群优化的随机进化计算方法来确定认知源节点的最优发送功率。
（4）研究基于正交空时块编码（OSTBC）的主次协作频谱共享技术。依据OSA方式，SU可接入经感知为空闲的信道。我们考虑了由一条单天线PU链路与单天线SU链路构成的基本干扰信道，提出了一种SU与PU互相协作的频谱共享方案。在此方案中，当PU数据包未被其接收端正确接收时，SU主动作为PU的协作中继节点在下一时隙通过OSTBC的方式协助PU再次转发该数据包，通过SU协作形成PU发送端的发送分集，降低PU数据包的中断概率，创造出更多的空闲时隙。从而，利用这些空闲时隙的SU链路的吞吐量可提高。我们推导了在对PU信号的错误检测概率等于零与不等于零两种情况下SU链路吞吐量的表达式。
With the rapid development of wireless communications, the demand for spectrum isaccelerating, which makes spectrum a scarce resource. However, the fixed spectrum allocationpolicy currently adopted renders a great portion of the licensed spectrum severely under-utilized,which has become a bottleneck for the deployment of future wireless communications. In recentyears, the rising cognitive radio (CR) technology enables unauthorized users or the so-calledsecondary users (SUs) sharing spectrum with licensed users or the so-called primary users (PUs),thus can improve spectrum utilization and provide bandwidth for new emerging wireless servicesand applications.
Multiple-In Multiple-Out (MIMO) brings spatial diversity and multiplexing, can improve thetransmission reliability and spectral efficiency in wireless communication systems, and is one of thekey technologies for future wireless communications. By introducing MIMO into cognitive radio,cognitive radio will be granted etra spatial degrees of freedom besides time and frequency whileaccessing PU’s spectrum. Therefore, it appears particularly important to combine MIMO andcognitive radio, investigate MIMO networks in cognitive radio environment, namely cognitiveMIMO networks.
For the purpose of guaranteeing the QoS of PUs, SUs should protect normal signaltransmissions of PUs. In principle, SUs can utilize the licensed spectrum of PUs in the twofollowing manners; one is called opportunistic spectrum access (OSA), SUs only access thelicensed spectrum which is not currently occupied by PUs, and the other one is concurrent spectrumaccess (CSA), SUs coexist with PUs in the licensed spectrum but should restrain the interferencecaused to PUs under a predefined low threshold, i.e., satisfy the interference/interferencetemperature constraint. In this dissertation, we focus on cognitive MIMO networks, in which theSUs are equipped with multiple antennas or virtual multi-antenna arrays are formed through usercooperation. We investigate the resource allocation and management problems in cognitive MIMOnetworks. More specifically, on the premise of guaranteeing the QoS of PUs, we deal with problemsof optimally deploying SUs’ communication resources such as transmit power, beamformingweights and precoding matrices in order to most efficiently utilize the licensed spectrum, e.g., achieve maximum information rate, signal to noise ratio or throughput. The main work andcontributions of this dissertation can be generalized as follows.
(1) Optimum transmit covariance matrix design for cognitive MIMO multiple accesschannels (MACs). We consider a cognitive MIMO MAC consisting of multiple multi-antenna SUtransmitters and a multi-antenna common receiver. We investigate, subject to transmit power andinterference temperature constraints, how to design the transmit covariance matrices of SUs suchthat the sum-rate of cognitive MIMO MAC is maximized. By exploiting the partial dualdecomposition technique to relax the interference temperature constraint, the original problem wasdecomposed into more tractable subproblems. An iterative algorithm, in which the dual variableupdate and the iterative water-filling computation were performed alternately, was proposed toobtain the optimum transmit covariance matrices that achieved the maximum sum-rate.
(2) Waveform adaptation for cognitive MIMIO interference channels (ICs). We consider acognitive MIMO IC formed by multiple independent SU links. Each link consists of a pair ofmulti-antenna SU nodes. From a non-cooperative game theoretic viewpoint, we investigate theNash equilibrium (NE) problem of SU links trying to maximize individual information rates bywaveform adaptation subject to transmit power and interference temperature constraints. Bytransforming the NE problem into equivalent variational inequality problem, we prove the existenceof the NE and provide sufficient conditions for the uniqueness of the NE. A decentralized iterativewater-filling algorithm (IWFA) with punishing price, called MIMO-CR IWFA, is proposed to solvethe above NE problem; the pricing mechanism is used to satisfy the interference-temperatureconstraint while SU links achieving the Nash equilibrium. The conditions for the convergence ofMIMO-CR IWFA are also provided. Simulation results show MIMO-CR IWFA can satisfy theinterference-temperature constraint perfectly and is fast convergent.
(3) Optimal source power control and relay cooperative beamforming in cognitive relaynetworks. We consider an amplify and forward cognitive radio network consisting of two terminalnodes and several relay nodes, in which, to achieve the spatial diversity gain, cognitive relay nodesform a virtual multiple antenna array and forward cognitive source node signal through cooperativebeamforming. Two interference temperature constraints, strict and loose, are considered, the formerconstraint requires the interference generated by cognitive nodes in both broadcast and relay phasesunder a predetermined interference threshold and the latter one requires the average interference generated in the two phases under the threshold. We investigate, subject to interference temperatureconstraint or both interference temperature and transmit power constraints, how to optimizecognitive source transmit power and cognitive relay beamforming weights in order to maximize thesignal-to-noise ratio (SNR) at the cognitive destination.
We show the SNR maximization problem subject to interference temperature constraint can beconverted into a generalized Rayleigh quotient problem, the optimal relay beamforming weights areequal to the eigenvector corresponding to the maximum eigenvalue of a matrix associated withchannel coefficients and source transmit power. For the strict case, analytical solutions of theoptimal source transmit power and relay beamforming weights are provided. For the loose case, astringent interval containing the optimal source transmit power is deduced and a steepest descentbased algorithm is used to achieve the optimal solution efficiently.
For the problem subject to interference temperature and transmit power constraints, we firstaddress the problem of optimal cooperative beamforming under a given source transmit power. Thisproblem is decomposed into a series of quadratically constrained quadratic programming (QCQP)problems through Dinkelbach type method. By utilizing semidefinite relaxation technique, theQCQP problems are relaxed into more tractable convex semidefinite programming (SDP) problemswhich can be solved by interior point method. Secondly, an evolutionary computation techniquebased on particle swarm optimization is used to achieve the optimum source transmit power.
(4) Spectrum sharing with cooperation between PUs and SUs based on orthogonalspace-time block coding (OSTBC). According to OSA model, the secondary user can access thechannel only when sensed idle. We investigate the spectrum-sharing problem in a basic CR scenarioconsisting of a PU link and a SU link, propose a spectrum-sharing scheme in which transmissiondiversity of the PU is formed by the SU actively acting as a cooperative relay of the PU throughOSTBC, and thus the PU’s probability of outage is reduced and the SU’s available idle slots areincreased. We derive the closed-form expression of the SU link’s throughput in our proposedscheme.
MIMO技术可带来空间分集与复用增益，能够显著地提高无线通信系统的传输可靠性以及频谱效率，是未来无线通信的关键技术之一。同时，将MIMO技术引入到认知无线电网络中，将在时间、频率之外为认知无线电接入授权频谱的行为带来额外的空间自由度。因此，对MIMO与认知无线电相结合，研究认知无线电环境下的MIMO网络，即认知MIMO网络，显得尤其重要。
在认知无线电环境中，为保护PU的信号传输，SU不能对PU造成有害干扰。基本上，SU利用PU频谱的方式可分为以下两种：一种是机会频谱接入（OSA），SU只接入未被PU占用的授权频谱；另一种是共存频谱接入（CSA），SU与PU同时共存，但需要抑制SU信号对PU的干扰在给定范围内，即满足干扰/干扰温度约束。
本文关注认知MIMO网络，其中SU装备多根天线或通过用户间协作形成虚拟多天线。本文研究不同网络架构下的认知MIMO网络的资源分配与管理问题。具体而言，我们考虑在保障PU的QoS的前提下，如何合理配置通信资源，包含发送功率控制、波束成形权值与预编码矩阵设计，从而使SU能够在对PU干扰最小化条件下，最有效的共享授权频谱，如从中获取最大的信息速率、信噪比（SNR）与吞吐量等。本文主要工作和贡献可概述如下：
（1）提出一种认知MIMO多址接入信道（MAC）中的最优发送协方差矩阵设计算法。我们考虑由多个多天线SU发送端与一个多天线SU共同接收端构成的认知MIMO MAC场景。研究了在给定SU节点发送功率与干扰温度约束下，如何选择SU的发送协方差矩阵使得认知MIMO MAC的速率之和最大化，解决优化问题时，利用部分对偶分解技术松弛干扰温度约束，将原始和速率最大化问题分解为两个相互联系的子问题。文中提出一种迭代算法，通过交替进行对偶变量更新与顺序迭代注水运算求解分解后的子问题，得到使得和速率最大的最优SU发送协方差矩阵。
（2）研究认知MIMO干扰信道（IC）中的波形自适应技术。我们考虑由多个独立的SU节点对构成的认知MIMO IC中的波形自适应问题，其中每个节点对都包含有一个多天线SU发送端与多天线SU接收端。我们使用了非合作博弈理论，在发送功率以及PU干扰温度共同约束下，针对多个SU为最大化自身链路信息速率进行的波形自适应进行建模，将其表示为Nash均衡问题；通过将Nash均衡问题转换为等价的变分不等式问题，推导了Nash均衡唯一存在的充分条件；提出了一种带价格系数的分布式发送功率迭代注水算法，即MIMO-CRIWFA，用以解决上述Nash均衡问题；通过价格机制，即使用惩罚价格使得所有参与通信的SU改变发送波形，控制对PU的叠加干扰，从而在满足PU的干扰温度约束时达到Nash均衡，最大化各自信息速率；同时给出了MIMO-CR IWFA算法收敛到唯一Nash均衡解的条件。
（3）研究认知中继网络中的最优源功率控制与协作波束成形技术。我们考虑多个单天线的SU通过协作组成虚拟多天线，来获得多天线系统所具有的空间分集性能，这样的SU虚拟多天线系统即认知中继网络。我们分别研究了在干扰温度约束下以及干扰温度与发送功率共同约束下的SU源节点功率控制与SU中继节点协作波束成形问题，优化的目标是使得SU目的节点的SNR最大。1）干扰温度约束下的源功率控制与中继协作波束成形问题被转化为仅与源发送功率相关的广义Rayleigh商问题，最优波束成形权值为一个与源发送功率以及信道系数相关的矩阵的最大特征值所对应的特征向量，原始多变量问题被简化为求解源节点最优发送功率的单变量问题。文中给出了严格干扰温度约束下的解析解，对于宽松干扰温度约束，文中进一步给出了包含最优发送功率的紧缩区间，并在该区间内使用了基于最速下降的梯度方法求解。2）对于发送功率与干扰温度共同约束，我们首先解决在给定任意认知源节点发送功率时，如何计算相应的认知中继最优协作波束成形权值。通过Dinkelbach型方法，这个问题被转化为一系列二次约束二次规划（QCQP）问题。利用半定松弛技术，这些QCQP问题被转变为易于解决的半定规划（SDP）问题从而可通过内点方法求解。随后，我们使用了一种基于粒子群优化的随机进化计算方法来确定认知源节点的最优发送功率。
（4）研究基于正交空时块编码（OSTBC）的主次协作频谱共享技术。依据OSA方式，SU可接入经感知为空闲的信道。我们考虑了由一条单天线PU链路与单天线SU链路构成的基本干扰信道，提出了一种SU与PU互相协作的频谱共享方案。在此方案中，当PU数据包未被其接收端正确接收时，SU主动作为PU的协作中继节点在下一时隙通过OSTBC的方式协助PU再次转发该数据包，通过SU协作形成PU发送端的发送分集，降低PU数据包的中断概率，创造出更多的空闲时隙。从而，利用这些空闲时隙的SU链路的吞吐量可提高。我们推导了在对PU信号的错误检测概率等于零与不等于零两种情况下SU链路吞吐量的表达式。
With the rapid development of wireless communications, the demand for spectrum isaccelerating, which makes spectrum a scarce resource. However, the fixed spectrum allocationpolicy currently adopted renders a great portion of the licensed spectrum severely under-utilized,which has become a bottleneck for the deployment of future wireless communications. In recentyears, the rising cognitive radio (CR) technology enables unauthorized users or the so-calledsecondary users (SUs) sharing spectrum with licensed users or the so-called primary users (PUs),thus can improve spectrum utilization and provide bandwidth for new emerging wireless servicesand applications.
Multiple-In Multiple-Out (MIMO) brings spatial diversity and multiplexing, can improve thetransmission reliability and spectral efficiency in wireless communication systems, and is one of thekey technologies for future wireless communications. By introducing MIMO into cognitive radio,cognitive radio will be granted etra spatial degrees of freedom besides time and frequency whileaccessing PU’s spectrum. Therefore, it appears particularly important to combine MIMO andcognitive radio, investigate MIMO networks in cognitive radio environment, namely cognitiveMIMO networks.
For the purpose of guaranteeing the QoS of PUs, SUs should protect normal signaltransmissions of PUs. In principle, SUs can utilize the licensed spectrum of PUs in the twofollowing manners; one is called opportunistic spectrum access (OSA), SUs only access thelicensed spectrum which is not currently occupied by PUs, and the other one is concurrent spectrumaccess (CSA), SUs coexist with PUs in the licensed spectrum but should restrain the interferencecaused to PUs under a predefined low threshold, i.e., satisfy the interference/interferencetemperature constraint. In this dissertation, we focus on cognitive MIMO networks, in which theSUs are equipped with multiple antennas or virtual multi-antenna arrays are formed through usercooperation. We investigate the resource allocation and management problems in cognitive MIMOnetworks. More specifically, on the premise of guaranteeing the QoS of PUs, we deal with problemsof optimally deploying SUs’ communication resources such as transmit power, beamformingweights and precoding matrices in order to most efficiently utilize the licensed spectrum, e.g., achieve maximum information rate, signal to noise ratio or throughput. The main work andcontributions of this dissertation can be generalized as follows.
(1) Optimum transmit covariance matrix design for cognitive MIMO multiple accesschannels (MACs). We consider a cognitive MIMO MAC consisting of multiple multi-antenna SUtransmitters and a multi-antenna common receiver. We investigate, subject to transmit power andinterference temperature constraints, how to design the transmit covariance matrices of SUs suchthat the sum-rate of cognitive MIMO MAC is maximized. By exploiting the partial dualdecomposition technique to relax the interference temperature constraint, the original problem wasdecomposed into more tractable subproblems. An iterative algorithm, in which the dual variableupdate and the iterative water-filling computation were performed alternately, was proposed toobtain the optimum transmit covariance matrices that achieved the maximum sum-rate.
(2) Waveform adaptation for cognitive MIMIO interference channels (ICs). We consider acognitive MIMO IC formed by multiple independent SU links. Each link consists of a pair ofmulti-antenna SU nodes. From a non-cooperative game theoretic viewpoint, we investigate theNash equilibrium (NE) problem of SU links trying to maximize individual information rates bywaveform adaptation subject to transmit power and interference temperature constraints. Bytransforming the NE problem into equivalent variational inequality problem, we prove the existenceof the NE and provide sufficient conditions for the uniqueness of the NE. A decentralized iterativewater-filling algorithm (IWFA) with punishing price, called MIMO-CR IWFA, is proposed to solvethe above NE problem; the pricing mechanism is used to satisfy the interference-temperatureconstraint while SU links achieving the Nash equilibrium. The conditions for the convergence ofMIMO-CR IWFA are also provided. Simulation results show MIMO-CR IWFA can satisfy theinterference-temperature constraint perfectly and is fast convergent.
(3) Optimal source power control and relay cooperative beamforming in cognitive relaynetworks. We consider an amplify and forward cognitive radio network consisting of two terminalnodes and several relay nodes, in which, to achieve the spatial diversity gain, cognitive relay nodesform a virtual multiple antenna array and forward cognitive source node signal through cooperativebeamforming. Two interference temperature constraints, strict and loose, are considered, the formerconstraint requires the interference generated by cognitive nodes in both broadcast and relay phasesunder a predetermined interference threshold and the latter one requires the average interference generated in the two phases under the threshold. We investigate, subject to interference temperatureconstraint or both interference temperature and transmit power constraints, how to optimizecognitive source transmit power and cognitive relay beamforming weights in order to maximize thesignal-to-noise ratio (SNR) at the cognitive destination.
We show the SNR maximization problem subject to interference temperature constraint can beconverted into a generalized Rayleigh quotient problem, the optimal relay beamforming weights areequal to the eigenvector corresponding to the maximum eigenvalue of a matrix associated withchannel coefficients and source transmit power. For the strict case, analytical solutions of theoptimal source transmit power and relay beamforming weights are provided. For the loose case, astringent interval containing the optimal source transmit power is deduced and a steepest descentbased algorithm is used to achieve the optimal solution efficiently.
For the problem subject to interference temperature and transmit power constraints, we firstaddress the problem of optimal cooperative beamforming under a given source transmit power. Thisproblem is decomposed into a series of quadratically constrained quadratic programming (QCQP)problems through Dinkelbach type method. By utilizing semidefinite relaxation technique, theQCQP problems are relaxed into more tractable convex semidefinite programming (SDP) problemswhich can be solved by interior point method. Secondly, an evolutionary computation techniquebased on particle swarm optimization is used to achieve the optimum source transmit power.
(4) Spectrum sharing with cooperation between PUs and SUs based on orthogonalspace-time block coding (OSTBC). According to OSA model, the secondary user can access thechannel only when sensed idle. We investigate the spectrum-sharing problem in a basic CR scenarioconsisting of a PU link and a SU link, propose a spectrum-sharing scheme in which transmissiondiversity of the PU is formed by the SU actively acting as a cooperative relay of the PU throughOSTBC, and thus the PU’s probability of outage is reduced and the SU’s available idle slots areincreased. We derive the closed-form expression of the SU link’s throughput in our proposedscheme.
引文
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